关键词: 智能故障诊断, 云/边缘协同, 卷积神经网络, 迁移学习

Abstract: Existing bearing fault diagnosis technology still has the following two problems:First,features need to be extracted manually in traditional diagnostic methods,which is time-consuming and unstable;Second,huge amounts of computing resources and time are needed in Convolutional Neural Networks(CNN)model training,which can not fulfill the real-time response requirements of fault diagnosis.To solve above problems,a cloud/edge collaborated fault diagnosis solution with real-time response ability for bearing was proposed.The experiment showed that this method had higher accuracy and lower time cost than the method without cloud/edge cooperation when the sample amount was small.The improved bearing fault diagnosis algorithm achieved higher diagnosis accuracy.Through transfer learning of the improved algorithm and edge-side collaboration,individualized product adaptability and real-time diagnosis ability of the fault diagnosis algorithm were enhanced.

Key words: intelligent fault diagnosis, cloud/edge collaboration, convolutional neural networks, transfer learning